Publications

Siddarth Singh, Omayma Mahjoub, Ruan John de Kock, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Arnu Pretorius (2024). How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning. eXplainable AI approaches for deep reinforcement learning (XAI4DRL) Workshop @ AAAI, 2024.

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Omayma Mahjoub, Ruan John de Kock, Siddarth Singh, Wiem Khlifi, Abidine Vall, Kale-ab Tessera, Rihab Gorsane, Arnu Pretorius (2024). Efficiently Quantifying Individual Agent Importance in Cooperative MARL. (Oral) eXplainable AI approaches for deep reinforcement learning (XAI4DRL) Workshop @ AAAI, 2024.

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Kale-ab Tessera, Callum Tilbury, Sasha Abramowitz, Ruan de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius (2023). Generalisable Agents for Neural Network Optimisation. WANT@ NeurIPS 2023 and OPT NeurIPS 2023 workshops.

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Andries Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas Barrett, Arnu Pretorius (2023). Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. Deep Generative Models for Health Workshop NeurIPS 2023.

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Claude Formanek, Callum Rhys Tilbury, Jonathan Shock, Kale-ab Tessera, Arnu Pretorius (2023). Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning. (Oral) Workshop on Reincarnating Reinforcement Learning at ICLR 2023.

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Kale-ab Tessera, Chiratidzo Matowe, Arnu Pretorius, Benjamin Rosman, Sara Hooker (2022). Just-in-Time Sparsity: Learning Dynamic Sparsity Schedules. Dynamic Neural Networks ICML Workshop 2022.

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Ibrahim Salihu Yusuf, Kale-ab Tessera, Thomas Tumiel, Zohra Slim, Amine Kerkeni, Sella Nevo, Arnu Pretorius (2021). On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa. AI + HADR 2021 and ML4D 2021 NeurIPS Workshops.

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Arnu Pretorius, Kale-ab Tessera, Andries P. Smit, Claude Formanek, St John Grimbly, Kevin Eloff, Siphelele Danisa, Lawrence Francis, Jonathan Shock, Herman Kamper, Willie Brink, Herman Engelbrecht, Alexandre Laterre, Karim Beguir (2021). Mava: a research framework for distributed multi-agent reinforcement learning.

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Kale-ab Tessera, Sara Hooker, Benjamin Rosman (2021). Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization. Sparsity in Neural Networks Workshop 2021.

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Kale-ab Tessera (2021). On Sparsity In Deep Learning: The Benefits And Pitfalls Of Sparse Neural Networks And How To Learn Their Architectures. Faculty of Science, University of the Witwatersrand.

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